{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/108352"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/108352","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Models and analysis of a stochastic neural source coder","abstract":"Information transfer in neurons takes place through action potentials (spikes) which are metabolically expensive. A neural coding approach was developed by Johnson et al. (2016) that is optimal, high-fidelity, energy-efficient and well matches the experimental spiking behavior of real neurons. This coder, called a neural source-coder, uses an adaptive threshold to internally reconstruct the stimulus. The spikes are timed to minimize coding error. These spikes are generated by a deterministic firing rule. However, some random variability in spike timing is observed in real data. It seems reasonable to account for the variability by adding a stochastic component to the deterministic model. Previously, the source-coding neuron used a constant threshold for generating spikes, while the stochastic neural encoder uses a partially randomized threshold. In this thesis we explore this random component and its success in explaining and recreating experimentally obtained spike-train statistics (from P-type electrosensory afferents of weakly electric fish). We also take a close look at the growth in variance of inter-spike intervals (ISIs) and the deviation of the stochastic source-coding neuron from an ideal DC-block system with infinite memory. The stochastic source-coding neuron model was able to achieve very accurate reconstructions of P-type weakly electric fish spike-time statistics (inter-spike interval histograms, serial correlation coefficients) with a very simple model consisting of only four free tuning parameters. We were also able to demonstrate a markedly slower growth in variance consistent with experimental data but which Poisson spike trains fail to capture. We were also able to derive mathematical correspondence for the observed experimental behavior such as the SCC trends, the rate of growth in variance of the ISIs and the power spectrum of the spike trains at low frequencies. The simulations back the mathematical findings illustrating the success of the model at creating statistically accurate and realistic spike trains.","abstract_html":"Information transfer in neurons takes place through action potentials (spikes) which are metabolically expensive. A neural coding approach was developed by Johnson et al. (2016) that is optimal, high-fidelity, energy-efficient and well matches the experimental spiking behavior of real neurons. This coder, called a neural source-coder, uses an adaptive threshold to internally reconstruct the stimulus. The spikes are timed to minimize coding error. These spikes are generated by a deterministic firing rule. However, some random variability in spike timing is observed in real data. It seems reasonable to account for the variability by adding a stochastic component to the deterministic model. Previously, the source-coding neuron used a constant threshold for generating spikes, while the stochastic neural encoder uses a partially randomized threshold. In this thesis we explore this random component and its success in explaining and recreating experimentally obtained spike-train statistics (from P-type electrosensory afferents of weakly electric fish). We also take a close look at the growth in variance of inter-spike intervals (ISIs) and the deviation of the stochastic source-coding neuron from an ideal DC-block system with infinite memory. The stochastic source-coding neuron model was able to achieve very accurate reconstructions of P-type weakly electric fish spike-time statistics (inter-spike interval histograms, serial correlation coefficients) with a very simple model consisting of only four free tuning parameters. We were also able to demonstrate a markedly slower growth in variance consistent with experimental data but which Poisson spike trains fail to capture. We were also able to derive mathematical correspondence for the observed experimental behavior such as the SCC trends, the rate of growth in variance of the ISIs and the power spectrum of the spike trains at low frequencies. The simulations back the mathematical findings illustrating the success of the model at creating statistically accurate and realistic spike trains.","abstract_has_math":false,"creators":["Sidhu, Robin Singh"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Electrical & Computer Engr","degree_department":null,"school":null,"contributors":["Jones, Douglas L","Ratnam, Rama"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2020,"date_issued":"2020-08-27T00:51:34Z","date_published":"2020-08-27T00:51:34Z","updated_at":"2026-07-22T22:24:48Z","subjects":["neural coding","stochastic source-coding neuron","serial correlation coefficient","inter-spike interval","growth in variance","P-type afferent"],"languages":["en"],"rights":["Copyright 2020 Robin Singh Sidhu"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/108352","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Jones, Douglas L","Ratnam, Rama"]},{"key":"dc:creator","label":"Author","values":["Sidhu, Robin Singh"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2020-08-27T00:51:34Z","2022-08-27T00:51:40Z","2020-05-14","2020-05"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical & Computer Engr"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["neural coding","stochastic source-coding neuron","serial correlation coefficient","inter-spike interval","growth in variance","P-type afferent"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2020 Robin Singh Sidhu"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/108352"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Information transfer in neurons takes place through action potentials (spikes) which are metabolically expensive. A neural coding approach was developed by Johnson et al. (2016) that is optimal, high-fidelity, energy-efficient and well matches the experimental spiking behavior of real neurons. This coder, called a neural source-coder, uses an adaptive threshold to internally reconstruct the stimulus. The spikes are timed to minimize coding error. These spikes are generated by a deterministic firing rule. However, some random variability in spike timing is observed in real data. It seems reasonable to account for the variability by adding a stochastic component to the deterministic model. Previously, the source-coding neuron used a constant threshold for generating spikes, while the stochastic neural encoder uses a partially randomized threshold. In this thesis we explore this random component and its success in explaining and recreating experimentally obtained spike-train statistics (from P-type electrosensory afferents of weakly electric fish). We also take a close look at the growth in variance of inter-spike intervals (ISIs) and the deviation of the stochastic source-coding neuron from an ideal DC-block system with infinite memory. The stochastic source-coding neuron model was able to achieve very accurate reconstructions of P-type weakly electric fish spike-time statistics (inter-spike interval histograms, serial correlation coefficients) with a very simple model consisting of only four free tuning parameters. We were also able to demonstrate a markedly slower growth in variance consistent with experimental data but which Poisson spike trains fail to capture. We were also able to derive mathematical correspondence for the observed experimental behavior such as the SCC trends, the rate of growth in variance of the ISIs and the power spectrum of the spike trains at low frequencies. The simulations back the mathematical findings illustrating the success of the model at creating statistically accurate and realistic spike trains.","Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2022-05-01","The student, Robin Singh Sidhu, accepted the attached license on 2020-05-13 at 16:55.","The student, Robin Singh Sidhu, submitted this Thesis for approval on 2020-05-13 at 17:03.","This Thesis was approved for publication on 2020-05-14 at 14:06.","DSpace SAF Submission Ingestion Package generated from Vireo submission #15385 on 2020-08-25 at 17:44:27","Made available in DSpace on 2020-08-27T00:51:34Z (GMT). No. of bitstreams: 2 SIDHU-THESIS-2020.pdf: 879663 bytes, checksum: 7f202a30439b5f8d8efbdb6a9d16f879 (MD5) LICENSE.txt: 4214 bytes, checksum: 4bcefd6617ec53d4e05c2009f339de5f (MD5) Previous issue date: 2020-05-14","Embargo set by: Seth Robbins for item 115967 Lift date: 2022-08-27T00:51:40Z Reason: Author requested closed access (OA after 2yrs) in Vireo ETD system","Author requested closed access (OA after 2yrs) in Vireo ETD system","Limited"]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Models and analysis of a stochastic neural source coder"]}]}],"canonical_facts":{"dc:contributor":["Jones, Douglas L","Ratnam, Rama"],"dc:creator":["Sidhu, Robin Singh"],"dc:date":["2020-08-27T00:51:34Z","2022-08-27T00:51:40Z","2020-05-14","2020-05"],"dc:description":["Information transfer in neurons takes place through action potentials (spikes) which are metabolically expensive. A neural coding approach was developed by Johnson et al. (2016) that is optimal, high-fidelity, energy-efficient and well matches the experimental spiking behavior of real neurons. This coder, called a neural source-coder, uses an adaptive threshold to internally reconstruct the stimulus. The spikes are timed to minimize coding error. These spikes are generated by a deterministic firing rule. However, some random variability in spike timing is observed in real data. It seems reasonable to account for the variability by adding a stochastic component to the deterministic model. Previously, the source-coding neuron used a constant threshold for generating spikes, while the stochastic neural encoder uses a partially randomized threshold. In this thesis we explore this random component and its success in explaining and recreating experimentally obtained spike-train statistics (from P-type electrosensory afferents of weakly electric fish). We also take a close look at the growth in variance of inter-spike intervals (ISIs) and the deviation of the stochastic source-coding neuron from an ideal DC-block system with infinite memory. The stochastic source-coding neuron model was able to achieve very accurate reconstructions of P-type weakly electric fish spike-time statistics (inter-spike interval histograms, serial correlation coefficients) with a very simple model consisting of only four free tuning parameters. We were also able to demonstrate a markedly slower growth in variance consistent with experimental data but which Poisson spike trains fail to capture. We were also able to derive mathematical correspondence for the observed experimental behavior such as the SCC trends, the rate of growth in variance of the ISIs and the power spectrum of the spike trains at low frequencies. The simulations back the mathematical findings illustrating the success of the model at creating statistically accurate and realistic spike trains.","Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2022-05-01","The student, Robin Singh Sidhu, accepted the attached license on 2020-05-13 at 16:55.","The student, Robin Singh Sidhu, submitted this Thesis for approval on 2020-05-13 at 17:03.","This Thesis was approved for publication on 2020-05-14 at 14:06.","DSpace SAF Submission Ingestion Package generated from Vireo submission #15385 on 2020-08-25 at 17:44:27","Made available in DSpace on 2020-08-27T00:51:34Z (GMT). No. of bitstreams: 2 SIDHU-THESIS-2020.pdf: 879663 bytes, checksum: 7f202a30439b5f8d8efbdb6a9d16f879 (MD5) LICENSE.txt: 4214 bytes, checksum: 4bcefd6617ec53d4e05c2009f339de5f (MD5) Previous issue date: 2020-05-14","Embargo set by: Seth Robbins for item 115967 Lift date: 2022-08-27T00:51:40Z Reason: Author requested closed access (OA after 2yrs) in Vireo ETD system","Author requested closed access (OA after 2yrs) in Vireo ETD system","Limited"],"dc:format":["application/pdf"],"dc:identifier":["http://hdl.handle.net/2142/108352"],"dc:language":["en"],"dc:rights":["Copyright 2020 Robin Singh Sidhu"],"dc:subject":["neural coding","stochastic source-coding neuron","serial correlation coefficient","inter-spike interval","growth in variance","P-type afferent"],"dc:title":["Models and analysis of a stochastic neural source coder"],"dc:type":["text","Thesis"],"thesis:degree_discipline":["Electrical & Computer Engr"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:24:48Z"}